Papers › Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models
Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models
Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples
Transfer learning is commonly employed to leverage large, pre-trained models and perform fine-tuning for downstream tasks. The most prevalent pre-trained models are initially trained using ImageNet. However, their ability to generalize can vary across different data modalities. This study compares pre-trained Audio Neural Networks (PANNs) and ImageNet pre-trained models within the context of underwater acoustic target recognition (UATR). It was observed that the ImageNet pre-trained models slightly out-perform pre-trained audio models in passive sonar classification. We also analyzed the impact of audio sampling rates for model pre-training and fine-tuning. This study contributes to transfer learning applications of UATR, illustrating the potential of pre-trained models to address limitations caused by scarce, labeled data in the UATR domain.
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